Imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import mplfinance as mpf
import matplotlib.dates as mdates
import datetime as dt
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
from plotly.subplots import make_subplots
pio.renderers.default = "notebook"
pio.templates.default = "plotly_dark"
import gc
np.random.seed(42)
import warnings
warnings.filterwarnings("ignore")
plt.rcParams["figure.figsize"] = [12, 8]
Topics cum Notes
L5
Financial Innovation
Financial innovation is the process of creating new financial products, services, or processes. Financial innovation has come via advances in financial instruments, technology, and payment systems. Digital technology has helped to transform the financial services industry, changing how we save, borrow, invest, and pay for goods.
Some examples of financial innovation are: - Investment Crowdfunding is a way to source money for a company by asking a large number of backers to each invest a relatively small amount in it. In return, backers receive equity shares of the company. - Remittances are a form of financial innovation that allows people to send money to their families in other countries. This is done through a variety of channels, including banks, money transfer companies, and mobile money services. - Mobile Banking is a form of financial innovation that allows people to access their bank accounts through their mobile phones.
Some classic examples are below:
Limited Liability
The idea is that investors, in order to be encouraged to invest in businesses, should have protection against liability for what the managers of the business do. So, an investor should not be liable for the debts or mistake of the business. This is the idea of limited liability.
The idea was enuciated first in 1811 by New York State.
Limited liability is a type of legal structure for an organization where a corporate loss will not exceed the amount invested in a partnership or limited liability company (LLC). In other words, investors' and owners' private assets are not at risk if the company fails.
The limited liability feature is one of the biggest advantages of investing in publicly listed companies. While a shareholder can participate wholly in the growth of a company, their liability is restricted to the amount of the investment in the company, even if it subsequently goes bankrupt and has remaining debt obligations.
Inflation Indexed Debt/Securities
Instead of using a currency which is liable of changes, inflation indexed debt uses some index to tie the debt to that index.
An inflation-indexed security is a security that guarantees a return higher than the rate of inflation if it is held to maturity. Inflation-indexed securities link their capital appreciation, or coupon payments, to inflation rates.
An inflation-indexed security has its principal indexed to the Consumer Price Index (CPI), or some other nationally recognized inflation index, on a daily basis.
Unidad de Fomento
The Unidad de Fomento (UF) is a unit of account used in Chile. It is a non-circulating currency;the exchange rate between the UF and the Chilean peso is constantly adjusted for inflation so that the value of the Unidad de Fomento remains almost constant on a daily basis during low inflation.
It has become the preferred and predominant measure to determine the cost of real estate, values of housing and any secured loan, either private or of the Chilean government. Individual payments are made in Chilean pesos (the country's legal tender), according to the daily value of the UF.
This is an innovation to deal with inflation.
L6
Forecasting
Forecasting is the process of making predictions based on past and present data. Later these can be compared (resolved) against what happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results. Prediction is a similar but more general term.
Something that happended on past is not the represenative of what will happen in future.
Random Walk
Random walk theory suggests that changes in stock prices have the same distribution and are independent of each other. Therefore, it assumes the past movement or trend of a stock price or market cannot be used to predict its future movement. In short, random walk theory proclaims that stocks take a random and unpredictable path that makes all methods of predicting stock prices futile in the long run.
We have
where \(\epsilon\) is a noise and is completely unforcastable.
apple = pd.read_csv("Data/AAPL.csv", parse_dates=["Date"], index_col="Date")
google = pd.read_csv("Data/GOOG.csv", parse_dates=["Date"], index_col="Date")
apple.head()
apple = apple[apple.index >= pd.to_datetime("2022-01-01", format="%Y-%m-%d", utc=True)]
apple = apple[["Close"]]
apple.head()
px.line(apple, x=apple.index, y="Close", title="Apple Stock Price")
}}
// Listen for the clearing of the current output cell var outputEl = gd.closest('.output'); if (outputEl) {{ x.observe(outputEl, {childList: true}); }}
}) }; }); </script>
sigma = apple["Close"].std()
print("Standard Deviation: ", sigma)
